Improving the prediction of disulfide bonds in Eukaryotes with machine learning methods and protein subcellular localization | Zendy

Castrense Savojardo | Zendy; Piero Fariselli | Zendy; Monther Alhamdoosh | Zendy; Pier Luigi Martelli | Zendy; Andrea Pierleoni | Zendy; Rita Casadio | Zendy

Open Access

Improving the prediction of disulfide bonds in Eukaryotes with machine learning methods and protein subcellular localization

Author(s) -

Castrense Savojardo,

Piero Fariselli,

Monther Alhamdoosh,

Pier Luigi Martelli,

Andrea Pierleoni,

Rita Casadio

Publication year - 2011

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btr387

Subject(s) - disulfide bond , subcellular localization , computer science , cysteine , artificial intelligence , chemistry , biochemistry , cytoplasm , enzyme

Disulfide bonds stabilize protein structures and play relevant roles in their functions. Their formation requires an oxidizing environment and their stability is consequently depending on the redox ambient potential, which may differ according to the subcellular compartment. Several methods are available to predict cysteine-bonding state and connectivity patterns. However, none of them takes into consideration the relevance of protein subcellular localization.

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